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An Alternative algorithm for the Symmetry Classification of Ordinary Differential Equations

Sound & Vibration 2022年第1期56卷 65-76页

作者： Yi Tian Jing Pang College of Data Science and Application Inner Mongolia University of TechnologyHohhot010080China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080China College of Science Inner Mongolia University of TechnologyHohhot010051China Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling Inner Mongolia University of TechnologyHohhot010051China

This is the first paper on symmetry classification for ordinary differential equations(ODEs)based on Wu’s *** carry out symmetry classification of two ODEs,named the generalizations of the Kummer-Schwarz equations which involving arbitrary ***,Lie algorithm is used to give the determining equations of symmetry for the given equations,which involving arbitrary ***,differential form Wu’s method is used to decompose determining equations into a union of a series of zero sets of differential characteristic sets,which are easy to be solved *** branch of the decomposition yields a class of symmetries and associated *** algorithm makes the classification become direct and *** Dimitrov Bozhkov,and Pammela Ramos da Conceição have used the Lie algorithm to give the symmetry classifications of the equations talked in this paper in *** this paper,we can find that the differential form Wu’s method for symmetry classification of ODEs with arbitrary function(parameter)is effective,and is an alternative method.

关键词： Kummer-Schwarz equation ordinary differential equations(ODEs) differential form Wu’s method

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Multi-scale cyclical similarity prototype refinement for few-shot breast ultrasound image segmentation 17

Multi-scale cyclical similarity prototype refinement for few...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Ou, Yingfeng Yang, Xing Zhang, Jian Jian, Caiqing Wang, Lihui College of Computer Science and Technology Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data Engineering Research Center of Text Computing Ministry of Education Guiyang China

ISBN: (纸本)9798350387384

Few-shot learning based methods can address the reliance on large-scale labeled samples in current breast tumor segmentation. However, previous methods typically rely on a few support samples to extract abstract, coarse-grained semantic prototypes, without considering the interaction between support and query data. In this study, we aim to provide each query image with relevant and information-rich support information. By integrating the difficult point selection module and the multi-scale cyclical prototype refinement module, we progressively refine coarse-grained support prototypes into more informative, fine-grained class prototypes, thereby better matching the highly variable breast ultrasound query data. Experiments on two datasets demonstrate that our proposed multi-scale cyclical similarity prototype refinement network (CSPRNet) achieves remarkable improvements compared to previous state-of-the-art methods. Specifically, using ResNet50 as the backbone, we achieve 57.93%/57.92% and 57.58%/59.24% mIoU for 1-shot and 5-shot segmentation, respectively, on public and private datasets, outperforming the suboptimal method by 8.18%/2.55% and 2.77%/4.48%. © 2024 IEEE.

关键词： Ultrasonic imaging

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Development of machine learning-based models to predict 10-year risk of cardiovascular disease:a prospective cohort study

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Stroke & Vascular Neurology 2023年第6期8卷 475-485页

作者： Jia You Yu Guo Ju-Jiao Kang Hui-Fu Wang Ming Yang Jian-Feng Feng Jin-Tai Yu Wei Cheng Institute of Science and Technology for Brain-Inspired Intelligence Department of NeurologyHuashan HospitalState Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceFudan UniversityShanghaiChina Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan UniversityMinistry of EducationShanghaiChina Zhangjiang Fudan International Innovation Center Fudan UniversityShanghaiChina School of Data Science Fudan UniversityShanghaiChina Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence Zhejiang Normal UniversityZhejiangChina Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center Fudan UniversityShanghaiChina

Background Previous prediction algorithms for cardiovascular diseases(CVD)were established using risk factors retrieved largely based on empirical clinical *** study sought to identify predictors among a comprehensive variable space,and then employ machine learning(ML)algorithms to develop a novel CVD risk prediction *** From a longitudinal population-based cohort of UK Biobank,this study included 473611 CVD-free participants aged between 37 and 73 years *** implemented an ML-based data-driven pipeline to identify predictors from 645 candidate variables covering a comprehensive range of health-related factors and assessed multiple ML classifiers to establish a risk prediction model on 10-year incident *** model was validated through a leave-one center-out *** During a median follow-up of 12.2 years,31466 participants developed CVD within 10 years after baseline visits.A novel UK Biobank CVD risk prediction(UKCRP)model was established that comprised 10 predictors including age,sex,medication of cholesterol and blood pressure,cholesterol ratio(total/high-density lipoprotein),systolic blood pressure,previous angina or heart disease,number of medications taken,cystatin C,chest pain and pack-years of *** model obtained satisfied discriminative performance with an area under the receiver operating characteristic curve(AUC)of 0.762±0.010 that outperformed multiple existing clinical models,and it was well-calibrated with a Brier Score of 0.057±***,the UKCRP can obtain comparable performance for myocardial infarction(AUC 0.774±0.011)and ischaemic stroke(AUC 0.730±0.020),but inferior performance for haemorrhagic stroke(AUC 0.644±0.026).Conclusion ML-based classification models can learn expressive representations from potential high-risked CVD participants who may benefit from earlier clinical decisions.

关键词： cardiovascular prediction learning

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DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland Instance Segmentation 17

DFGET: Displacement-Field Assisted Graph Energy Transmitter ...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Jian, Caiqing Qin, Yongbin Wang, Lihui Ye, Chen Cheng, Xinyu Guizhou University Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350387384

Gland instance segmentation is an essential but challenging task in the diagnosis of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constraint. However, how to deal with the problems of gland adhesion and inaccurate boundary in segmenting the complex samples remains a challenge. In this work, we propose a novel geometric feature named displacement field (DF), which can represent the geometric information such as shape, size, and orientation of the glands. Based on the explicit constraint of DF, a displacement-field assisted graph energy transmitter (DFGET) framework is proposed for solving the above problems. Using such graph framework, the gland semantic segmentation map and the displacement field of the graph nodes are estimated with two graph network branches. With the constraint of DF, a graph cluster module is presented to improve the intra-class feature consistency and inter-class feature discrepancy, as well as to separate the adherent glands. Extensive comparison and ablation experiments on the GlaS dataset demonstrate the effectiveness of the displacement field and the superiority of DFGET. Compared to the best comparative model, DFGET increases the object-Dice and object-F1 score by 2.27% and 3.11% respectively, while decreases the object-HD by 15.58, achieving state-of-the-art performance, which indicates that DFGET can serve as an auxiliary tool for the diagnosis of adenocarcinoma. © 2024 IEEE.

关键词： Semantic Segmentation

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Assessing the effectiveness of the intervention measures of COVID-19 in China based on dynamical method

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Infectious Disease Modelling 2023年第1期8卷 159-171页

作者： Xiaomeng Wei Mingtao Li Xin Pei Zhiping Liu Juan Zhang Complex Systems Research Center Shanxi University030006ShanxiChina Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention 030006ShanxiChina School of Mathematical Sciences Shanxi University030006ShanxiChina College of Mathematics Taiyuan University of Technology030024ShanxiChina School of Data Science and Technology North University of China030051ShanxiChina

Normalized interventions were implemented in different cities in China to contain the outbreak of COVID-19 before December ***,the differences in the intensity and timeliness of the implementations lead to differences in final size of the *** the outbreak of COVID-19 in three representative cities Xi'an,Zhengzhou and Yuzhou in January 2022,as examples,we develop a compartmental model to describe the spread of novel coronavirus and implementation of interventions to assess concretely the effectiveness of Chinese interventions and explore their impact on epidemic *** applying reported human confirmed cases to verify the rationality of the model,we apply the model to speculate transmission trend and length of concealed period at the initial spread phase of the epidemic(they are estimated as 10.5,7.8,8.2 days,respectively),to estimate the range of basic reproduction number(2.9,0.7,1.6),and to define two indexes(transmission rate vt and controlled rate vc)to evaluate the overall effect of the *** is shown that for Zhengzhou,vc is always more than v t with regular interventions,and Xi'an take 8 days to achieve vc>v t twice as long as Yuzhou,which can interpret the fact that the epidemic situation in Xi'an was more *** carrying out parameter values,it is concluded that in the early stage,strengthening the precision of close contact tracking and frequency of large-scale nucleic acid testing of non-quarantined population are the most effective on controlling the outbreaks and reducing final ***,if the close contact tracking strategy is sufficiently implemented,at the late stage largescale nucleic acid testing of non-quarantined population is not essential.

关键词： COVID-19 Close contact tracking Large-scale nucleic acid testing Dynamics The basic reproduction number Sensitivity analysis

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THE RIEMANN PROBLEM WITH DELTA INITIAL data FOR THE NON-ISENTROPIC IMPROVED AW-RASCLE-ZHANG MODEL

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Acta Mathematica Scientia 2023年第1期43卷 237-258页

作者：蒋伟峰陈停停李彤王振 College of Science Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang ProvinceChina Jiliang UniversityHangzhou310018China School of Mathematics and Physics China University of GeosciencesWuhan430074China Department of Mathematics The University of IowaIowaIA52246USA Center for Mathematical Sciences and Department of Mathematics Wuhan University of TechnologyWuhan430070China

In this paper,we study the Radon measure initial value problem for the nonisentropic improved Aw-Rascle-Zhang *** arbitrary convex F(u)in this model we construct the Riemann solutions by elementary waves andδ-shock waves using the method of generalized characteristic *** obtain the solutions constructively for initial data containing the Dirac measure by taking the limit of the solutions for that with three piecewise ***,we analyze different kinds of wave interactions,including the interactions of theδ-shock waves with elementary waves.

关键词： Riemann problem non-isentropic improved Aw-Rascle-Zhang model δ-shock wave wave interactions traffic flow

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A Time-series Prediction algorithm Based on a Hybrid Model

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Recent Advances in Computer science and Communications 2023年第1期16卷 3-17页

作者： Cao, Danyang Ma, Jinfeng Sun, Ling Ma, Nan School of Information Science and Technology North China University of Technology Beijing100144 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing100144 China National Satellite Meteorological Center Beijing100081 China Key Laboratory of Radiometric Calibration and Validation for Environmental Satellite Beijing100081 China College of Robotics Beijing Union University Beijing100101 China

Background: In reality, time series is composed of several basic components, which have linear, nonlinear and non-stationary characteristics at the same time. Directly using a single model will show some limitations and the prediction accuracy is difficult to improve. Methods: We propose a mixed forecasting model based on time series decomposition, namely STL-EEMD-LSTM model. First, we use STL filtering algorithm to decompose the time series to obtain the trend component, seasonal component and the remainder component of the time series;then we use EEMD to decompose the seasonal component and the remainder component to obtain multiple sub-sequences. After this, we reconstruct the new seasonal component and the remainder component according to the fluctuation frequency of the sub-sequence. Finally, we use LSTM to build a prediction model for each component obtained by decomposition. Results: We applied the proposed model to simulation data and the time series of satellite calibration parameters and found that the hybrid prediction model proposed in this paper has high prediction accuracy. Conclusion: Therefore, we believe that our proposed model is more suitable for the prediction of time series with complex components. © 2023 Bentham science Publishers.

关键词： Time series

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Salix-Leaf: Find Main Veins of Signal Clusters for Practical Parallel Decoding

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IEEE Transactions on Mobile Computing 2025年

作者： Li, Yajun Zhao, Jumin Li, Dengao Wu, Hejun Xu, Shuang Bai, Ruiqin Taiyuan University of Technology College of Electronic Information and Optical Engineering Taiyuan China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan China Taiyuan University of Technology College of Computer Science and Technology College of Data Science Taiyuan China Sun Yat-sen University Department of Computer Science Guangzhou China

Parallel decoding of backscatter improves communication throughput by enabling concurrent transmission of backscatter tags. In practical applications of parallel decoding, it is extremely difficult to distinguish collided signals in superclusters where multiple signal clusters overlap. Existing methods are usually effective for superclusters with uniformly distributed signals. Nevertheless, there are many more scenarios in which signals in superclusters tend to gather unevenly, and existing methods cannot work. Such uneven clustering of signals occurs due to the following two possible causes: (1) signal-strengthdifferences (SSDs) among tags;or (2) cluster drifting (CD) driven by interferences from other objects within communication environments. This paper proposes a novel scheme called SalixLeaf, which aims to identify the main veins of signal clusters to address this problem of superclusters with unevenly distributed signals. Salix-Leaf identifies the main vein of each signal cluster for fine-grained clustering so that the direction of the main veins can be used to verify the accuracy of clustering. In addition, SalixLeaf employs a supercluster decomposer that divides signals into different segments for clustering analysis, enhancing robustness and practicability. Experimental results show that Salix-Leaf achieves a 1.2-fold increase in throughput and a 25% reduction in bit error rate (BER) compared to the state-of-the-art. © 2002-2012 IEEE.

关键词： Backscattering

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The Additive Generators of <i>n</i>-Dimensional Overlap Functions

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Journal of Applied Mathematics and Physics 2021年第8期9卷 2159-2169页

作者： Hai Xie College of Science Guilin University of Technology Guilin China Center for Data Analysis and Algorithm Technology Guilin University of Technology Guilin China

In this paper, we introduce the concepts of additive generators and additive generator pair of n-dimensional overlap functions, in order to extend the dimensionality of overlap functions from 2 to n. We mainly discuss... 详细信息

关键词： Overlap Functions n-Dimensional Overlap Functions Additive Generators Additive Generator Pair

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APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System

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IEEE Access 2023年 11卷 74000-74020页

作者： Javed, Safdar Hussain Ahmad, Maaz Bin Asif, Muhammad Akram, Waseem Mahmood, Khalid Das, Ashok Kumar Shetty, Sachin College of Computing and Information Sciences Karachi75190 Pakistan Lahore Garrison University Department of Computer Science Lahore54810 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan International Institute of Information Technology at Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center Department of Modeling SuffolkVA23435 United States Old Dominion University Center for Cybersecurity Education and Research SuffolkVA23435 United States

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm. © 2013 IEEE.

关键词： Real time systems

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