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For LEO Satellite Networks: Intelligent Interference Sensing and Signal Reconstruction Based on Blind Separation Technology

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China Communications 2024年第2期21卷 85-95页

作者： Chengjie Li Lidong Zhu Zhen Zhang The key laboratory for Computer Systems of state ethnic affairs commission School of Computer Science and Technology(Southwest Minzu University)Chengdu 610000China National key laboratory of Science and Technology on Communications(University of electronic Science and Technology of China) Chengdu 610000China College of Computer Science(Sichuan University) Chengdu 610000China

In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signals make the receiving ability of the signal receiver worse, the signal processing ability weaker,and the anti-interference ability of the communication system lower. Aiming at the above problems, to save communication resources and improve communication efficiency, and considering the irregularity of interference signals, the underdetermined blind separation technology can effectively deal with the problem of interference sensing and signal reconstruction in this scenario. In order to improve the stability of source signal separation and the security of information transmission, a greedy optimization algorithm can be executed. At the same time, to improve network information transmission efficiency and prevent algorithms from getting trapped in local optima, delete low-energy points during each iteration process. Ultimately, simulation experiments validate that the algorithm presented in this paper enhances both the transmission efficiency of the network transmission system and the security of the communication system, achieving the process of interference sensing and signal reconstruction in the LEO satellite communication system.

关键词： blind source separation greedy optimization algorithm interference sensing LEO satellite communication networks signal reconstruction

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For Mega-Constellations: Edge Computing and Safety Management Based on Blockchain Technology

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China Communications 2024年第2期21卷 59-73页

作者： Zhen Zhang Bing Guo Chengjie Li College of Computer Science Sichuan UniversityChengdu 610064China The key laboratory for Computer Systems of state ethnic affairs commission School of Computer Science and TechnologySouthwest Minzu UniversityChengdu 610225China National key laboratory of Science and Technology on Communications University of Electronic Science and Technology of ChinaChengdu 611731China

In mega-constellation Communication Systems, efficient routing algorithms and data transmission technologies are employed to ensure fast and reliable data transfer. However, the limited computational resources of satellites necessitate the use of edge computing to enhance secure *** edge computing reduces the burden on cloud computing, it introduces security and reliability challenges in open satellite communication channels. To address these challenges, we propose a blockchain architecture specifically designed for edge computing in mega-constellation communication systems. This architecture narrows down the consensus scope of the blockchain to meet the requirements of edge computing while ensuring comprehensive log storage across the network. Additionally, we introduce a reputation management mechanism for nodes within the blockchain, evaluating their trustworthiness, workload, and efficiency. Nodes with higher reputation scores are selected to participate in tasks and are appropriately incentivized. Simulation results demonstrate that our approach achieves a task result reliability of 95% while improving computational speed.

关键词： blockchain consensus mechanism edge computing mega-constellation reputation management

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User Purchase Intention Prediction Based on Improved Deep Forest

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 661-677页

作者： Yifan Zhang Qiancheng Yu Lisi Zhang School of Computer Science and Engineering North Minzu UniversityYinchuan750030China laboratory of Graphics and Images of the state ethnic affairs commission North Minzu UniversityYinchuan750030China

Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter *** address this issue,based on the deep forest algorithm and further integrating evolutionary ensemble learning methods,this paper proposes a novel Deep Adaptive Evolutionary Ensemble(DAEE)*** model introduces model diversity into the cascade layer,allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior ***,this paper optimizes the methods of obtaining feature vectors,enhancement vectors,and prediction results within the deep forest algorithm to enhance the model’s predictive *** demonstrate that the improved deep forest model not only possesses higher robustness but also shows an increase of 5.02%in AUC value compared to the baseline ***,its training runtime speed is 6 times faster than that of deep models,and compared to other improved models,its accuracy has been enhanced by 0.9%.

关键词： Purchase prediction deep forest differential evolution algorithm evolutionary ensemble learning model selection

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A DFT study on Pt single atom loaded COF for efficient photocatalytic CO_(2)reduction

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Journal of Materials Science & Technology 2024年第3期170卷 87-94页

作者： Fei Xie Chuanbiao Bie Jian Sun Zhenyi Zhang Bicheng Zhu laboratory of Solar Fuel Faculty of Materials Science and ChemistryChina University of GeosciencesWuhan430078China key laboratory of New Energy and Rare Earth Resource Utilization of state ethnic affairs commission Key Laboratory of Photosensitive Materials and Devices of Liaoning ProvinceSchool of Physics and Materials EngineeringDalian Minzu UniversityDalian116600China

Photocatalytic CO_(2)reduction provides a promising strategy for the alleviation of greenhouse effect and energy shortage *** single atom modification is an effective method to improve the CO_(2)reduction performance of covalent organic framework(COF)photocatalysts,while the intrinsic mechanism is not revealed in ***,a Pt-COF photocatalytic system was constructed by embedding Pt single atom in TFPT-TMT-COF(TFPT:1,3,5-tris(4-formylphenyl)-triazine,TMT:2,4,6-trimethyl-1,3,5-triazine).Based on density functional theory(DFT)calculation,the geometric structure and electronic property of Pt-COF were investigated,and the CO_(2)adsorption and reduction reaction process on Pt-COF were *** results show that the Pt atom can be steadily anchored in COF via the formation of Pt-N and Pt-C ***,there is strong electronic interaction between Pt and *** incorporation of Pt atom benefits the CO_(2)reduction activity of COF from multiple aspects:(ⅰ)reducing the band gap and improving light absorption;(ⅱ)enhancing CO_(2)adsorption and activating the CO_(2)molecule;(ⅲ)decreasing the energy barrier in the hydrogenation step of CO_(2)→COOH,thus facilitating CO_(2)conversion to CO;(ⅳ)inhibiting the hydrogenation of CO,thereby increasing the selectivity of *** work brings a novel insight into the excellent CO_(2)reduction performance of Pt modified COF,and provides useful references for the design of single atom photocatalysts.

关键词： Photocatalysis Carbon dioxide reduction Covalent organic framework Pt single atom Theoretical calculation

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MCIF-Transformer Mask RCNN:Multi-Branch Cross-Scale Interactive Feature Fusion Transformer Model for PET/CT Lung Tumor Instance Segmentation

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Computers, Materials & Continua 2024年第6期79卷 4371-4393页

作者： Huiling Lu Tao Zhou School of Medical information&Engineering Ningxia Medical UniversityYinchuan750004China School of Computer Science and Engineering North Minzu UniversityYinchuan750021China key laboratory of Image and Graphics Intelligent Processing of state ethnic affairs commission NorthMinzu UniversityYinchuan750021China

The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided ***,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are *** solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung *** pixel dependence relationship is established in local and non-local fields to improve the model perception ***,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained ***,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic ***,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image *** results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this *** method has positive significance for the detection of lung tumors.

关键词： PET/CT images instance segmentation mask RCNN interactive fusion transformer

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MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

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Computers, Materials & Continua 2024年第6期79卷 4863-4882页

作者： Tao Zhou Yujie Guo Caiyue Peng Yuxia Niu Yunfeng Pan Huiling Lu School of Computer Science and Engineering North Minzu UniversityYinchuan750021China School of Medical information&Engineering Ningxia Medical UniversityYinchuan750004China key laboratory of Image and Graphics Intelligent Processing of state ethnic affairs commission North Minzu UniversityYinchuan750021China

Computer-aided diagnosis of pneumonia based on deep learning is a research ***,there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images.A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this *** main innovations are as follows:Firstly,the Multi-scale Residual Feature Extraction Module(MRFEM)is designed to effectively extract multi-scale *** MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features ***,the Multi-scale Directional Feature Perception Module(MDFPM)is designed,which uses a three-branch structure of different sizes convolution to transmit direction feature layer by layer,and focuses on the target region to enhance the feature ***,the Axial Compression Former Module(ACFM)is designed to perform global calculations to enhance the perception ability of global features in different *** verify the effectiveness of the MSD-Net,comparative experiments and ablation experiments are carried *** the COVID-19 RADIOGRAPHY DATABASE,the Accuracy,Recall,Precision,F1 Score,and Specificity of MSD-Net are 97.76%,95.57%,95.52%,95.52%,and 98.51%,*** the chest X-ray dataset,the Accuracy,Recall,Precision,F1 Score and Specificity of MSD-Net are 97.78%,95.22%,96.49%,95.58%,and 98.11%,*** model improves the accuracy of lung image recognition effectively and provides an important clinical reference to pneumonia Computer-Aided Diagnosis.

关键词： Pneumonia X-ray image ResNet multi-scale feature direction feature transformer

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Long-term variation of Arctic Sudden Stratospheric Warmings(SSW) and potential causes

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Earth and Planetary Physics 2024年第5期8卷 742-752页

作者： QingRan Li ShaoDong Zhang KaiMing Huang ChunMing Huang Yun Gong WenTao Tang Zheng Ma School of electronic information Wuhan University Guizhou Normal University key laboratory of Geospace Environment and Geodesy Ministry of Education state key laboratory of information Engineering in Surveying Mapping and Remote Sensing School of electronic information Engineering Jingchu University of Technology

Utilizing the European Centre for Medium-Range Weather Forecast(ECMWF) Reanalysis v5(ERA5), for the first time, we have confirmed close links among Sudden Stratospheric Warmings(SSWs) in the Northern Hemisphere(NH), the polar vortices, and stratospheric Planetary Waves(PWs) by analyzing and comparing their trends. Interestingly, within overall increasing trends, the duration and strength of SSWs exhibit increasing and decreasing trends before and after the winter of 2002, respectively. To reveal possible physical mechanisms driving these trends, we analyzed the long-term trends of the winter(from December to February) polar vortices and of stratospheric PWs with zonal wave number 1. Notably, our results show that in all three time periods(the entire period of 41winters, 1980 to 2020, and the two subperiods — 1980-2002 and 2002-2020) enhancing SSWs were always accompanied by weakening winter polar vortices and strengthening polar PWs like Stationary Planetary Waves(SPWs) and 16-day waves, and vice versa. This is the first proof, based on ERA5 long-term trend data, that weakening polar vortices and enhancing stratospheric PWs(especially SPWs) could cause an increase in SSWs.

关键词： sudden stratospheric warmings stationary planetary waves 16-day waves polar vortrices long-term trend correlation coefficient

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Multi-Sensor Fusion Adaptive Estimation for Nonlinear Under-observed System with Multiplicative Noise

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Chinese Journal of electronics 2024年第1期33卷 282-292页

作者： Yongpeng CUI Xiaojun SUN School of electronic Engineering Heilongjiang University key laboratory of information Fusion Estimation and Detection

The adaptive fusion estimation problem was studied for the multi-sensor nonlinear under-observed systems with multiplicative noise. A one-step predictor with state update equations was designed for the virtual state with virtual noise first of all. An extended incremental Kalman filter(EIKF) was then proposed for the nonlinear under-observed systems. Furthermore, an adaptive filtering method was given for optimization. The fusion adaptive incremental Kalman filter weighted by scalar was finally proposed. The comparison analysis was made to verify the optimization of the state estimation using adaptive filtering method in the filtering process.

关键词： Adaptive systems information filters Matrices Adaptive estimation Kalman filters state estimation Nonlinear systems

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Guided-YNet: Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network

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Computers, Materials & Continua 2024年第9期80卷 4813-4832页

作者： Tao Zhou Yunfeng Pan Huiling Lu Pei Dang Yujie Guo Yaxing Wang School of Computer Science and Engineering North Minzu UniversityYinchuan750021China School of Medical information&Engineering Ningxia Medical UniversityYinchuan750004China key laboratory of Image and Graphics Intelligent Processing of state ethnic affairs commission NorthMinzuUniversityYinchuan750021China

Multimodal lung tumor medical images can provide anatomical and functional information for the same *** as Positron Emission Computed Tomography(PET),Computed Tomography(CT),and *** to utilize the lesion anatomical and functional information effectively and improve the network segmentation performance are key *** solve the problem,the Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network(Guide-YNet)is proposed in this ***,a double-encoder single-decoder U-Net is used as the backbone in this model,a single-coder single-decoder U-Net is used to generate the saliency guided feature using PET image and transmit it into the skip connection of the backbone,and the high sensitivity of PET images to tumors is used to guide the network to accurately locate ***,a Cross Scale Feature Enhancement Module(CSFEM)is designed to extract multi-scale fusion features after ***,a Cross-Layer Interactive Feature Enhancement Module(CIFEM)is designed in the encoder to enhance the spatial position information and semantic ***,a Cross-Dimension Cross-Layer Feature Enhancement Module(CCFEM)is proposed in the decoder,which effectively extractsmultimodal image features through global attention and multi-dimension local *** proposed method is verified on the lung multimodal medical image datasets,and the results showthat theMean Intersection overUnion(MIoU),Accuracy(Acc),Dice Similarity Coefficient(Dice),Volumetric overlap error(Voe),Relative volume difference(Rvd)of the proposed method on lung lesion segmentation are 87.27%,93.08%,97.77%,95.92%,89.28%,and 88.68%,*** is of great significance for computer-aided diagnosis.

关键词： Medical image segmentation U-Net saliency feature guidance cross-modal feature enhancement cross-dimension feature enhancement

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Fast measurement and prediction method for electromagnetic susceptibility of receiver

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Journal of Systems Engineering and electronics 2024年第2期35卷 275-285页

作者： CHEN Yan LU Zhonghao LIU Yunxia state key laboratory of Complex Electromagnetic Environment Effects on electronics and information System College of Electronic Science and TechnologyNational University of Defense TechnologyChangsha 410073China state key laboratory of Astronautic Dynamics Xi’an 710043China

Aiming at evaluating and predicting rapidly and accurately a high sensitivity receiver’s adaptability in complex electromagnetic environments,a novel testing and prediction method based on dual-channel multi-frequency is proposed to improve the traditional two-tone ***,two signal generators are used to generate signals at the radio frequency(RF)by frequency scanning,and then a rapid measurement at the intermediate frequency(IF)output port is carried out to obtain a huge amount of sample data for the subsequent ***,the IF output response data are modeled and analyzed to construct the linear and nonlinear response constraint equations in the frequency domain and prediction models in the power domain,which provide the theoretical criteria for interpreting and predicting electromagnetic susceptibility(EMS)of the *** experiment performed on a radar receiver confirms the reliability of the method proposed in this *** shows that the interference of each harmonic frequency and each order to the receiver can be identified and predicted with the sensitivity *** on this,fast and comprehensive evaluation and prediction of the receiver’s EMS in complex environment can be efficiently realized.

关键词： electromagnetic susceptibility(EMS) receiver dualchannel multi-frequency nonlinear response frequency domain power domain

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