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Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke:development and validation of a hybrid machine learning model

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Stroke & Vascular Neurology 2024年第6期9卷 631-639页

作者： Ximing Nie Jinxu Yang Xinxin Li Tianming Zhan Dongdong Liu Hongyi Yan Yufei Wei Xiran Liu Jiaping Chen Guoyang Gong Zhenzhou Wu Zhonghua Yang Miao Wen Weibin Gu Yuesong Pan Yong Jiang Xia Meng Tao Liu Jian Cheng Zixiao Li Zhongrong Miao Liping Liu Department of Neurology Capital Medical UniversityBeijingChina China National Clinical Research Center for Neurological Diseases Beijing Tiantan HospitalBeijingChina School of Computer and Communication Engineering University of Science and TechnologyBeijingChina Department of Radiology Beijing Tiantan HospitalBeijingChina Key Laboratory of Biomechanics and Mechanobiology Ministry of EducationSchool of Biological Science and Medical Engineering International Research Institute for Multidisciplinary ScienceBeihang UniversityBeijingChina Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Computer Science and EngineeringBeihang UniversityBeijingChina Department of Interventional Neuroradiology Beijing Tiantan HospitalCapital Medical UniversityBeijingChina

Background Identification of futile recanalisation following endovascular therapy(EVT)in patients with acute ischaemic stroke is both crucial and ***,we present a novel risk stratification system based on hybrid machine learning method for predicting futile *** Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management *** models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following *** optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation *** Using a hybrid feature selection approach,we trained and tested multiple classifiers on two independent patient cohorts(n=1122)to develop a hybrid machine learning-based prediction *** model demonstrated superior discriminative ability compared with other models and scoring systems(area under the curve=0.80,95%CI 0.73 to 0.87)and was transformed into a web application(RESCUE-FR Index)that provides a risk stratification system for individual prediction(accessible online atfr-index.biomind.cn/RESCUE-FR/).Conclusions The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT.

关键词： database prediction optimal

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Securing STAR-FC-RIS empowered integrated sensing and multiuser communications against target eavesdropping

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science China(Information sciences) 2024年第11期67卷 335-346页

作者： Shuying LIN Yulong ZOU Fu XIAO Bin LI School of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications School of Computer Science Nanjing University of Posts and Telecommunications Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications

This paper investigates a simultaneous-transmitting-and-reflecting fully-connected reconfigurable intelligent surface(STAR-FC-RIS) empowered integrated sensing and multiuser communications(ISAMC) network, where a dual-functional radar-communication base station detects a malicious radar target nearby and communicates with multiple legitimate users on the other side of the STAR-RIS. We utilize an integrated architecture that combines the fully-connected(FC)-RIS, an emerging type of beyond-diagonal(BD)-RIS, with the time-switching(TS)-STAR-RIS to enhance both the sensing and communications at the cost of possible target intercepting and propose the simultaneous-transmitting-and-reflecting fully-connected RIS(STAR-FCR) schemes to strike a balance between sensing and communications performance. Thereafter, observing the security-reliability performance tradeoff of the downlink ISAMC, we conduct closed-form analyses to compare COPs of round-robin scheduling(RS) and multiuser scheduling(MS) with the aid of a TS-based STAR-FC-RIS. Furthermore, we derive closed-form expressions of the sensing outage probability, communications outage probability(COP), and communications intercept probability, where an average of the three probabilities is exploited to obtain an optimized time allocation(OTA) of the *** results verify that the STAR-FCR-MS scheme outperforms the STAR-FCR-RS scheme in terms of sensing reliability and communications security. Moreover, an OTA remarkably enhances the overall performance of the STAR-FCR schemes of ISAMC systems.

关键词： simultaneous-transmitting-and-reflecting reconfigurable intelligent surface (RIS) fully-connected beyond-diagonal RIS integrated sensing and communications multiuser scheduling security

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Ultra Dense Satellite-Enabled 6G Networks:Resource Optimization and Interference Management

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China communications 2023年第10期20卷 262-275页

作者： Xiangnan Liu Haijun Zhang Min Sheng Wei Li Saba Al-Rubaye Keping Long School of Computer&Communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology BeijingBeijing 100083China School of Telecommunications Engineering Xidian UniversityXi’an 710071China State Radio Monitoring Center of China Beijing 100037China School of Aerospace Transport and Manufacturing Cranfield UniversityMK430ALUK

With the evolution of the sixth generation(6G)mobile communication technology,ample attention has gone to the integrated terrestrial-satellite *** paper notes that four typical application scenarios of integrated terrestrial-satellite networks are integrated into ultra dense satellite-enabled 6G networks *** the subchannel and power allocation schemes for the downlink of the ultra dense satellite-enabled 6G heterogeneous networks are *** mobile edge computing(SMEC)with edge caching in three-layer heterogeneous networks serves to reduce the link traffic of ***,a scheme for interference management is presented,involving quality-of-service(QoS)and co-tier/cross-tier interference *** simulation results show that the proposed schemes can significantly increase the total capacity of ultra dense satellite-enabled 6G heterogeneous networks.

关键词： satellite-enabled 6G networks network architecture resource optimization interference management

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LGDF-Net: Local and Global Feature Based Dual-Branch Fusion Networks for Deepfake Detection

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5489-5500页

作者： Long, Min Liu, Zhenyu Zhang, Le-Bing Peng, Fei Guangzhou University School of Electronics and Communication Engineering Guangdong Guangzhou510006 China Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410114 China Huaihua University School of Computer and Artificial Intelligence Huaihua418000 China Guangzhou University School of Artificial Intelligence Guangdong Guangzhou510006 China

With the rapid development of Deepfake technology, social security is facing great challenges. Although numerous Deepfake detection algorithms based on traditional CNN frameworks perform well on specific datasets, they still suffer from overfitting due to an over-reliance on localized artifact information. This limitation leads to degraded detection performance across diverse datasets. To address this issue, this study proposes a dual-branch fusion network called LGDF-Net. LGDF-Net uses a dual-branch structure to process the local artifact features and global texture features generated by Deepfake separately, preserving their unique characteristics. Specifically, the local compression branch utilizes a specially designed local compression module (LCM) that allows the network to focus more accurately on key regions of localized artifacts in Deepfake faces. The global expansion branch enhances the analysis of the global facial context through a global expansion module (GEM), which captures image context information and subtle texture features more comprehensively. Additionally, the proposed multi-scale feature extraction module (MSFE) delves into image features at various scales, enriching the extraction of detailed information. Finally, the multi-level feature fusion strategy (MLFF) improves the integration of local and global features through multiple layers, enabling the network to learn the intrinsic connections between these two types of features. A series of experimental validations demonstrate that the proposed scheme outperforms many existing detection networks in terms of accuracy and generalization ability. © 1991-2012 IEEE.

关键词： Feature extraction

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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science China(Information sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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An Improved Dual-Path Attention Mechanism with Enhanced Discriminative Power for Asset Comparison Based on ResNet50 5

An Improved Dual-Path Attention Mechanism with Enhanced Disc...

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5th International Conference on Advances in Electrical, Electronics and Computing technology, EECT 2025

作者： Jing, Ying Zhang, Li School of Computer Science Beijing Information Science and Technology University Beijing China School of Management Science and Engineering Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798331541545

With the wide application of deep convolutional neural networks (CNNs), higher requirements are put forward for feature extraction and discriminant ability in asset comparison tasks. The rapid development of CNNs has significantly promoted the performance improvement of computer vision tasks, in which ResNet50, as the mainstream feature extraction network, effectively alleviates the problem of gradient disappearance in deep networks by means of residual learning. However, despite ResNet50's excellent performance in many visual tasks, there is still room for improvement in the effectiveness of feature expression and the mechanism of attention to key areas in response to changes in complex scenes. Therefore, this paper puts forth a new network architecture with improved dual-path attention mechanism and discriminative feature enhancement module based on ResNet50 (DADF-ResNet50). This method strengthens important features in channel and spatial dimensions respectively through the improved dual-path attention mechanism to improve the model's focusing ability on key asset areas. At the same time, the discriminating power enhancement module further optimizes the distinguishing ability of features and effectively improves the feature comparison performance between different assets. We design and implement the network architecture of this approach and evaluate it comprehensively in the asset comparison task. The experimental results show that the model has better distinguishing ability and feature extraction efficiency in diverse asset comparison scenarios, which is helpful to solve the asset identification problem in complex scenarios. © 2025 IEEE.

关键词： Deep neural networks

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Versatile Tasks on Integrated Aerial Platforms Using Only Onboard Sensors: Control, Estimation, and Validation

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IEEE Transactions on Robotics 2025年 41卷 3518-3538页

作者： Wang, Kaidi Lai, Ganghua Yu, Yushu Du, Jianrui Sun, Jiali Xu, Bin Franchi, Antonio Sun, Fuchun Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Beijing Institute of Technology School of Mechanical Engineering Beijing100081 China University of Twente Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics and Computer Science Enschede7500 AE Netherlands Tsinghua University Department of Computer Science and Technology China

Connecting multiple aerial vehicles to a rigid central platform through passive spherical joints holds the potential to construct a fully-actuated aerial platform. The integration of multiple vehicles enhances efficiency in tasks like mapping and object reconnaissance. This paper proposes a control and state estimation framework for the Integrated Aerial Platform (IAP), enabling it to perform versatile tasks like object reconnaissance and physical interactive tasks with only onboard sensors. In the framework, the 6D motion control serves as the low-level controller, while the high-level controller comprises a 6D admittance filter and a perception-aware attitude correction module. The 6D admittance filter, serving as the interaction controller, is adaptable for aerial interaction tasks. The perception-aware attitude correction algorithm is carefully designed by adopting a geometric Model Predictive Controller (MPC). This algorithm, incorporating both offline and online calculations, proves to be well-suited for the intricate dynamics of an IAP. A 6D direct wrench controller is also developed for the IAP. Notably, both the interaction controller and the direct wrench controller operate without reliance on force/torque sensors. Instead, a wrench observer algorithm is devised, considering external disturbances. Additionally, based on the kinematics constraints of the multiple aerials in the platform, a fusion algorithm for multiple Visual-Inertial Odometry (VIO) and kinematics constraints is developed, providing more accurate localization. A prototype of the IAP is constructed, and its capabilities are demonstrated through experiments including perception-aware object reconnaissance, aerial mapping, aerial peg-in-hole task, and 6D contact wrench generation. All experiments are conducted exclusively with onboard sensors. These tasks exemplify the merits of the proposed IAP and validate the effectiveness of the proposed control framework and fusion algorithm. © 2004-2012

关键词： State estimation

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Study on flood probability by neural network prediction based on the importance of random forest characteristics 3

Study on flood probability by neural network prediction base...

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3rd International Conference on Electrical, Electronics, and Information engineering, EEIE 2024

作者： Zhang, Yujun Kang, Yu Liu, Yupeng Fan, Rulong Duan, Jianfu School of Computer Science and Technology Taiyuan University of Science and Technology China School of Information and Communication Engineering Beijing University of Posts and Telecommunications China School of Economics and Management Taiyuan University of Technology China

ISBN: (纸本)9781510688179

In recent years, global climate change and human activities have intensified the frequency and severity of floods. Based on the big data of flood probability, combined with the index data of monsoon intensity, river management, deforestation, urbanization and climate change, this paper studies from the perspective of flood probability prediction. Firstly, this paper selects indicators and collects corresponding data, and processes the data. Then, the importance of index characteristics is evaluated by the random forest algorithm. Secondly, the number of final indicators is determined by kernel principal component analysis, and the final indicators are determined by combining the importance of features. Then, the number of classifications is determined by the AIC/BIC criterion, and the risk is classified into three categories: high, medium, and low, by GMM clustering, based on the EM algorithm. Finally, 60% of the data is used as the training set, 30% of the data as the verification set, and 10% of the data as the test set to divide the data set, and then it is brought into the neural network prediction model for prediction. Finally, by analyzing the clustering results and prediction results, it provides a scientific basis and a practical application scheme for disaster prevention and reduction. © The Authors.

关键词： Flood damage

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ANALYSIS OF TWO ANY ORDER SPECTRAL VOLUME METHODS FOR 1-D LINEAR HYPERBOLIC EQUATIONS WITH DEGENERATE VARIABLE COEFFICIENTS

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Journal of Computational Mathematics 2024年第6期42卷 1627-1655页

作者： Minqiang Xu Yanting Yuan Waixiang Cao Qingsong Zou College of Science Zhejiang University of TechnologyHangzhou 310023China School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510275China School of Mathematical Sciences Beijing Normal UniversityBeijing 100875China School of Computer Science and Engineering and Guangdong Province Key Laboratory of Computational ScienceSun Yat-sen UniversityGuangzhou 510275China

In this paper,we analyze two classes of spectral volume(SV)methods for one-dimensional hyperbolic equations with degenerate variable *** classes of SV methods are constructed by letting a piecewise k-th order(k≥1 is an integer)polynomial to satisfy the conservation law in each control volume,which is obtained by refining spectral volumes(SV)of the underlying mesh with k Gauss-Legendre points(LSV)or Radaus points(RSV)in each *** L^(2)-norm stability and optimal order convergence properties for both methods are rigorously proved for general non-uniform ***,we discover some very interesting superconvergence phenomena:At some special points,the SV flux function approximates the exact flux with(k+2)-th order and the SV solution itself approximates the exact solution with(k+3/2)-th order,some superconvergence behaviors for element averages errors have been also ***,these superconvergence phenomena are rigorously proved by using the so-called correction function *** theoretical findings are verified by several numerical experiments.

关键词： Spectral Volume Methods L^(2)stability Error estimates Superconvergence

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Automatic iterative diversity improvement for defect data generation 24

Automatic iterative diversity improvement for defect data ge...

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16th International Conference on computer Modeling and Simulation, ICCMS 2024

作者： Pi, Xiaoning Xie, Yulai Zhang, Yang Wang, Xiaohui Ren, Fang School of Computer and Communication Engineering University of Science and Technology Beijing China Hitachi China Research Laboratory China

ISBN: (纸本)9798400717215

In industrial production, visual defect inspection is crucial for ensuring product quality. However, due to difficulties in sample collection during defect inspection, there is often a scarcity of diverse defective samples, leading most data generation methods to fail in generating high-diversity data. Therefore, this paper proposes the Automatic Iterative Diversity Improvement training strategy to improve the diversity of generated data with only a few training samples, which can improve the performance and generalization of downstream tasks. During the training process, given the characteristics of visual defects, we design the Defect Diversity-based Picking and Evaluation to pick up diverse samples and assess the diversity regarding defect location and shape. Experiments show that the method can generate high-diversity defect samples with high quality for industrial defect scenarios even in zero-shot learning tasks. The effectiveness of the generated data is also verified by the down-streaming defect detection task. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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