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Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

Computers, Materials & Continua 2024年第8期80卷 3047-3065页

作者： Jianfeng Lu Caijin Li Xiangye Huang Chen Cui Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310000China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

The widespread availability of digital multimedia data has led to a new challenge in digital *** source camera identification algorithms usually rely on various traces in the capturing ***,these traces have become increasingly difficult to extract due to wide availability of various image processing *** Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera ***,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall *** this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these *** proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature *** representation is then fed into a subsequent camera fingerprint classification *** upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone ***,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.

关键词： Source camera identification camera forensics convolutional neural network feature fusion transformer block graph convolutional network

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Multiphysics Simulation Analysis of Excessive Circuit Resistance of Reactive Power Compensation Circuit Breaker 7

Multiphysics Simulation Analysis of Excessive Circuit Resist...

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7th International Conference on Electric Power Equipment - Switching Technology, ICEPE-ST 2024

作者： Jiang, Yuan Huang, Kai Zhang, Kaiyuan Cao, Rui Zhang, Qian Tong, Ziang School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing100083 China University of Liverpool Department of Electrical Engineering and Electronics LiverpoolL69 3GJ United Kingdom International Student Center University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9798350388947

In order to analyze the reasons for the excessive resistance of reactive power compensation SF6 circuit breaker in actual switching scenarios, firstly, the overvoltage and surge current situations during the switching process of shunt capacitor bank were simulated in this paper. Then, based on overvoltage and surge current data, a multi physics field simulation was conducted on the electromagnetic field, temperature field, and fluid field coupling of the arc chamber. The results show that the reignition overvoltage generated during the disconnection process of the shunt capacitor bank can reach up to 6 times the rated voltage of the system, and the surge current generated during the connection of the shunt capacitor bank can reach up to 16.8 times the rated current, which has a significant impact on the stable operation of the circuit breaker. The surge current generated during the connection process can cause temperature rise, with a maximum temperature close to 1000K. The reignition arc will burn the contact, and the maximum temperature of the contact can reach 7875 K. All of these situations will have an impact on the electrical contact performance of the contacts, which will cause the resistance of breaker circuit to exceed the standard. © 2024 IEEE.

关键词： Capacitor bank

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Characteristic Analysis of Intermediate Frequency Vacuum Arc Based on Stereo Vision Technology 5th

Characteristic Analysis of Intermediate Frequency Vacuum Arc...

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5th International Symposium on Plasma and Energy Conversion, iSPEC 2023

作者： Jiang, Yuan Zhang, Qian Ma, Suliang Wu, Jianwen Xia, Shangwen Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China International Student Center University of Science and Technology Beijing Beijing100083 China Energy Storage Technology Engineering Research Center North China University of Technology Beijing100144 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9789819722440

With the increasing demand of electric power for more-electric aircraft, current breaking and protection in intermediate frequency (360–800 Hz) AC power system becomes more and more difficult. Different from the case of power frequency at 50 Hz, the arc time is shortened due to the increase of current frequency, so that the anode spot is not significant when the intermediate frequency vacuum arc is generated. Therefore, a large number of metal droplets produced by contacts are important sources of metal vapor at post-arc stage, and the metal vapor will have a great influence on the recovery strength of dielectric. During the splashing process, the metal droplet continues to evaporate, and the Cu mass fraction on the surface of the droplet decreases from 65 to 10%. Stereo vision technology is used to calculate the velocity and acceleration of droplets sprayed by contacts in three-dimensional space, so as to obtain the pressure distribution in the intermediate frequency vacuum arc chamber during the process of post-arc breakdown. According to the parallax principle, the velocity of the droplets is about 10 m/s, and the acceleration is 105 m/s2, and the pressure gradient is 1.2 MPa/mm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Stereo vision

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Heavy Rainfall Prediction Model Using Sample Entropy Derived from GNSS-PWV and PSO-SVM 14th

Heavy Rainfall Prediction Model Using Sample Entropy Derived...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Wu, Fanming Li, Dengao Zhao, Jinhua Feng, Ran Shi, Danyang Zhang, Xinfang Zhao, Jumin College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

There is a growing interest to use Global Navigation Satellite System (GNSS) inversed PWV for heavy rainfall prediction. When heavy rainfall occurs, it requires the atmosphere to contain sufficient water vapour and undergo strong upward motion. However, existing models using GNSS-PWV for heavy rainfall prediction have not accounted for the effect of the complex motion of water vapour. In this paper, an hourly heavy rainfall prediction model using sample entropy derived from GNSS-PWV and PSO-SVM is proposed. The sample entropy of GNSS-PWV is used to measure the complexity of the water vapour movement process before heavy rainfall occurs. Meanwhile, combining GNSS-PWV time domain features and co-located meteorological data, the occurrence of hourly heavy rainfall events is predicted by support vector machine optimized with particle swarm. To verify the validity and feasibility of the proposed algorithm, Hong Kong's HKSC station in Sham Shui Po is used for the model to train and test. The result shows that probability of detection, false alarm rate and critical success index of the proposed algorithm in this paper have significantly improved over other heavy rainfall prediction models. © 2024, Aerospace Information Research Institute.

关键词： Support vector machines

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LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image 30

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Ronghuan Zhao, Jing Li, Ming Zou, Qingzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong University of Traditional Chinese Medicine School of Intelligence and Information Engineering Jinan China

ISBN: (纸本)9798331515966

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural Networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch Network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity. © 2024 IEEE.

关键词： Convolutional neural networks

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PSRDP:A Parallel Processing Method for Pulsar Baseband data

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Research in Astronomy and Astrophysics 2024年第1期24卷 300-310页

作者： Ya-Zhou Zhang Hai-Long Zhang Jie Wang Xin-Chen Ye Shuang-Qiang Wang Xu Du Han Wu Ting Zhang Shao-Cong Guo Meng Zhang Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Radio Astronomy Chinese Academy of SciencesNanjing 210008China National Astronomical Data Center Beijing 100101China Southeast University Nanjing 211189China School of Cyber Science and Engineering Qufu Normal UniversityQufu 273165China

To address the problem of real-time processing of ultra-wide bandwidth pulsar baseband data,we designed and implemented a pulsar baseband data processing algorithm(PSRDP)based on GPU parallel computing *** can perform operations such as baseband data unpacking,channel separation,coherent dedispersion,Stokes detection,phase and folding period prediction,and folding integration in GPU *** tested the algorithm using the J0437-4715 pulsar baseband data generated by the CASPSR and Medusa backends of the Parkes,and the J0332+5434 pulsar baseband data generated by the self-developed backend of the Nan Shan Radio *** obtained the pulse profiles of each baseband *** experimental analysis,we have found that the pulse profiles generated by the PSRDP algorithm in this paper are essentially consistent with the processing results of Digital Signal Processing Software for Pulsar Astronomy(DSPSR),which verified the effectiveness of the PSRDP ***,using the same baseband data,we compared the processing speed of PSRDP with DSPSR,and the results showed that PSRDP was not slower than DSPSR in terms of *** theoretical and technical experience gained from the PSRDP algorithm research in this article lays a technical foundation for the real-time processing of QTT(Qi Tai radio Telescope)ultra-wide bandwidth pulsar baseband data.

关键词： (stars:)pulsars:general methods:data analysis techniques:miscellaneous

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Deep Learning for Semantic Segmentation of Football Match Image 2

Deep Learning for Semantic Segmentation of Football Match Im...

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2nd International Conference on Innovations and Development of Information Technologies and Robotics, IDITR 2023

作者： Wu, Yutian Zhao, Wuqi Huang, Chen Xi, Yaming Li, Qing Wang, Heng University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing China Beijing International Studies University Department of Physical Education Beijing China University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing China

ISBN: (纸本)9798350345575

As one of the most popular sports, football has been a subject to growth and advancements in technology. The combination of football and artificial intelligence is expected to be used for intelligent football analysis. Image semantic segmentation is an important basis for image analysis and understanding. This paper proposes a deep learning-based image segmentation model for pixel-level classification of the video recordings frames of football matches. Every pixel of football video frame is classified into one of the 10 classes, e.g., players, ball, goal bar and several background scenes. In this paper, we first test a variety of CNN architectures and pre-Trained models and select the MobileNet-UNet architecture as our baseline. We note the severe unbalanced data distribution in football scene segmentation. To solve this problem, the weighted multi-class cross-entropy loss is adopted in training of MobileNet-UNet to redistribute the weights of classification loss, focusing on smaller foreground object classes and improving segmentation accuracy. We also propose to use image transformations and a random mixture sampling technique for training data augmentation to reduce model overfitting. The model is trained and validated in the well-Annotated Football Semantic Segmentation Open dataset. The proposed best model achieves 0.96 frequency weighted IoU and 0.90 mean IoU segmentation accuracy on validation set. © 2023 IEEE.

关键词： Semantic Segmentation

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Polarization imaging bi-attentional recursive residual super-resolution method 9

Polarization imaging bi-attentional recursive residual super...

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9th Symposium on Novel Photoelectronic Detection Technology and Applications

作者： Liu, Jiaqing Li, Yi Liang, Dong Xu, Guoming Zhou, Pucheng Ma, Jian School of Internet Anhui University Hefei China National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei China Anhui Provincial Key Laboratory of Polarized Light Imaging Detection Hefei China School of Electrical and Electronics Engineering Anhui Wenda University of Information Engineering Hefei China

ISBN: (纸本)9781510664432

Due to the limitation of the detector, the spatial resolution of the polarization image obtained by space-modulated full-polarization computed imaging is low. In general, there are a lot of valuable high-frequency components in low resolution space, but it is difficult to reconstruct high frequency information by deep learning method. Through the research of the previous super-resolution reconstruction method based on deep learning, it is found that it is difficult to obtain better lifting effect only by stacking residual blocks to build a deeper network. A polarization imaging bi-attention recursive residual network super-resolution reconstruction method is proposed. In the network structure, bi-attention recursive residual group is used as the deep feature extraction module, and the bi-attention module contained in this module is used to adaptively learn image features of different channels and different transformation Spaces in the deep network. In order to provide the concentrated learning efficiency of high-frequency information, jump connections are introduced into the network structure, and the shallow feature extraction and reconstruction of images are completed by a convolution layer and a sub-pixel convolution algorithm respectively. The experimental verification is carried out under the actual imaging system and simulation data, and compared with other methods in terms of visual effect and quantitative results. Experimental results verify that the proposed method can effectively reconstruct the contour structure and texture details of the detection target in subjective image quality, and is superior to the comparison method in objective evaluation index. © 2023 SPIE.

关键词： Feature extraction

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NegCPARBP: Enhancing Privacy Protection for Cross-Project Aging-Related Bug Prediction Based on Negative database

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IEEE Transactions on Emerging Topics in Computing 2025年第02期13卷 283-298页

作者： Zhao, Dongdong Liu, Zhihui Zhang, Fengji Liu, Lei Keung, Jacky Wai Yu, Xiao Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan China Wuhan University of Technology Chongqing Research Institute Chongqing China City University of Hong Kong Department of Computer Science Hong Kong Xi'an Jiaotong University School of Electronic Science and Engineering Xi'an China Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou China

The emergence of Aging-Related Bugs (ARBs) poses a significant challenge to software systems, resulting in performance degradation and increased error rates in resourceintensive systems. Consequently, numerous ARB prediction methods have been developed to mitigate these issues. However, in scenarios where training data is limited, the effectiveness of ARB prediction is often suboptimal. To address this problem, CrossProject Aging-Related Bug Prediction (CPARBP) is proposed, which utilizes data from other projects (i.e., source projects) to train a model aimed at predicting potential ARBs in a target project. However, the use of source-project data raises privacy concerns and discourages companies from sharing their data. Therefore, we propose a method called Cross-Project AgingRelated Bug Prediction based on Negative database (NegCPARBP) for privacy protection. NegCPARBP first converts the feature vector of a software file into a binary string. Secondly, the corresponding Negative dataBase (NDB) is generated based on this binary string, containing data that is significantly more expressive from the original feature vector. Furthermore, to ensure more accurate prediction of ARB-prone and ARB-free files based on privacy-protected data (i.e., maintain the data utility), we propose a novel negative database generation algorithm that captures more information about important features, using information gain as a measure. Finally, NegCPARBP extracts a new feature vector from the NDB to represent the original feature vector, facilitating data sharing and ARB prediction objectives. Experimental results on Linux, MySQL, and NetBSD datasets demonstrate that NegCPARBP achieves a high defense against attacks (privacy protection performance reaching 0.97) and better data utility compared to existing privacy protection methods. © 2025 IEEE.

关键词： Differential privacy

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Deformable registration framework for glioma images with absent correspondence based on auxiliary-image-aided intensity-consistency constraint

Deformable registration framework for glioma images with abs...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tang, Kun Wang, Lihui Yang, Menglong Xu, Jingwen Cheng, Xinyu Zhang, Jian Zhu, Yuemin Wei, Hongjiang Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 Lyon France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200240 China

ISBN: (纸本)9798350386226

Considering the tumor aggressive nature and the significant changes in anatomical structure, aligning the preoperative and follow up scans of glioma patients remains a challenge due to the presence of regions with absent correspondence. To address this challenge, this work proposed a novel bidirectional unsupervised deformable image registration framework for image pairs with missing correspondence based on an auxiliary-image-aided intensity-consistency constraint (ICC) strategy. Specifically, for any fixed and moving image pairs, we introduced an auxiliary image and warped it directly to fixed/moving image or warped it twice through a transition of moving/fixed image. By comparing the difference between these warped images, the weighting maps to identify and exclude regions with absent correspondence between fixed and moving image pairs can be generated. To verify the effectiveness of the proposed framework, we combined it with several deep learning-based registration models and tested it on BraTS-Reg challenge dataset, the results demonstrated that the proposed ICC strategy can improve the registration performance for all the models, with the improvement of average target registration error (TRE) and success rate (SR) being up to 44.9% and 66.7%, respectively. Comparing against the best existing forward-backward consistency strategy for dealing with missing correspondence registration, our auxiliary-image-aided ICC strategy can also decrease average TRE by 2.9%, demonstrating the superiority of the proposed framework. The present work is not limited to the glioma images, it can be used to address the registration problems for any image pairs with absent correspondence or inconsistent intensity. © 2024 IEEE.

关键词： Image registration

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