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2nd International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2022

作者： Yu, Hui Cai, Lanlan Yan, Xue Network Information Center Wuhan University of Technology Wuhan430070 China School of Mechanical and Electronic Engineering Wuhan University of Technology Wuhan430070 China School of Computer and Artificial Intelligence Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9781510660571

Diabetic retinopathy is a serious ocular complication caused by diabetes, has now become the main reason for the workforce blinding, using digital fundus image for diabetic retinopathy screening regularly is the key to preventing blindness. However, in the process of image acquisition, generation and transmission, it is susceptible to low light conditions and Gaussian noise, and it is difficult to detect small lesions and blood vessels in fundus images, which greatly reduces the accuracy of computer-aided diagnosis. Therefore, this paper proposes an image enhancement algorithm for digital fundus images, using adaptive clipping based on improved Canny edge detection to perform square clipping around fundus region. The improved CLAHE technique was used for low light enhancement to highlight the details of the lesions in the image. Aiming at the additive white Gaussian noise caused by medical image digitalize process, the fundus image denoising algorithm based on self-supervised EM-GMM is used to suppress the influence of noise by imposing sparsity constraint on covariance eigenvalues. The experiment shows that our methods achieve good performance in DDR dataset. © 2022 SPIE.

关键词： computer aided diagnosis

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Privacy-Preserving Collaborative Filtering Algorithm Based on Local Differential Privacy

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China Communications 2021年第11期18卷 42-60页

作者： Ting Bao Lei Xu Liehuang Zhu Lihong Wang Ruiguang Li Tielei Li School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China School of Cyberspace Science and Technology Beijing Institute of TechnologyBeijing 100081China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing 100029China China International Engineering Consulting Corporation Beijing 100048China

Mobile edge computing(MEC)is an emerging technolohgy that extends cloud computing to the edge of a *** has been applied to a variety of ***,MEC can help to reduce network delay and improve the service quality of recommendation *** a MEC-based recommendation system,users’rating data are collected and analyzed by the edge *** the servers behave dishonestly or break down,users’privacy may be *** solve this issue,we design a recommendation framework that applies local differential privacy(LDP)to collaborative *** the proposed framework,users’rating data are perturbed to satisfy LDP and then released to the edge *** edge servers perform partial computing task by using the perturbed *** cloud computing center computes the similarity between items by using the computing results generated by edge *** propose a data perturbation method to protect user’s original rating values,where the Harmony mechanism is modified so as to preserve the accuracy of similarity *** to enhance the protection of privacy,we propose two methods to protect both users’rating values and rating *** results on real-world data demonstrate that the proposed methods perform better than existing differentially private recommendation methods.

关键词： personalized recommendation collaborative filtering data perturbation privacy protection local differential privacy

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network View and Cognitive Mechanism for Virtual network Resource Management Based Intelligent

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Chinese Journal of Electronics 2025年第3期23卷 574-578页

作者： Xiaochun Wu Chunming Wu Bin Wang Yaguan Qian Julong Lan Computer Science College Zhejiang University Hangzhou China Institute of Network and Communication Engineering Zhejiang Gongshang University Hangzhou China National Digital Switching System Engineering & Technology R&D Center Zhengzhou China

network virtualization has been a particularly important driving force behind the development of NGN. Finding effective ways to control self-adapting virtual networks has become a research hot spot. This paper tries to find an efficient way in combining dynamic resource sensing with traffic character and requirements in virtual network, based on that an intelligent cognition based virtual network resources management model has been put forward. We design a network topology view by coloring network topology matrix with current resources model. We present an intelligent virtual resource management mechanism based on the proposed network topology view. Experiment results illustrate that the proposed mechanism can improve VN capacity by automatic resource adjust-ment, which can obtain high utilization of VN resources without increasing computational expenses.

关键词： Technological innovation network topology Computational modeling Force Dynamics network resource management Dynamic scheduling Sensors Virtualization Next generation networking

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Research on air interface interference recognition in mobile communication networks based on LSTM autoencoder 5

Research on air interface interference recognition in mobile...

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5th International Conference on Signal Processing and computer Science, SPCS 2024

作者： Sun, Hongwei Hu, Mingwei Wang, Baoji Chen, Yue Fu, Jingwen Huang, Jia Jiangsu Automation Research Institute Lianyungang222006 China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100094 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Beijing Sankuai Online Technology Co. Ltd. Beijing100102 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Due to the diverse nature of interference signals in mobile communication frequency bands, feature extraction is challenging, and the rapid and automated identification of behaviors such as illegal frequency usage and malicious interference are difficult. Moreover, evaluating the precise impact of interference signals on communication networks is challenging. In response to this problem, this paper proposes an air interface interference recognition technology for communication links based on base station measurement reporting data. It constructs an interference recognition model to achieve automatic interference identification and evaluates the accuracy of interference recognition. On the base station side, this paper utilizes channel measurement data such as signal strength, signal quality, and signal-to-noise ratio exported by the base station. It uses a Long Short-Term Memory (LSTM) autoencoder model to learn normal signal patterns for interference discrimination and evaluates the impact of interference on communication networks. This method achieves precise identification of fixed-frequency interference on the base station side and evaluates the impact of interference on communication networks. Experimental results show that the base station-side approach achieves an F1 score of 0.99 in identifying fixed-frequency interference, outperforming One-Class Support Vector Machine (OCSVM), Principal Component Analysis (PCA), and Isolation Forest (IForest) methods under similar conditions. © 2025 SPIE.

关键词： Base stations

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Smart Energy Management System Using Machine Learning

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computers, Materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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An Online Chronic Disease Prediction System Based on Incremental Deep Neural network

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computers, Materials & Continua 2021年第4期67卷 951-964页

作者： Bin Yang Lingyun Xiang Xianyi Chen Wenjing Jia School of Design Jiangnan UniversityWuxi214122China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and TechnologyNanjing210044China School of Electrical and Data Engineering University of Technology SydneySydney2000Australia

Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural ***,due to the complexity of the human body,there are still many challenges to face in that *** of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients,*** paper presents a novel chronic disease prediction system based on an incremental deep neural *** propensity of users suffering from chronic diseases can continuously be evaluated in an incremental *** time,the system can predict diabetes more and more accurately by processing the feedback *** diabetes prediction studies are based on a common dataset,the Pima Indians diabetes dataset,which has only eight input *** order to determine the correlation between the pathological characteristics of diabetic patients and their daily living resources,we have established an in-depth cooperation with a hospital.A Chinese diabetes dataset with 575 diabetics was ***’data collected by different sensors were used to train the network *** evaluated our system using a real-world diabetes dataset to confirm its *** experimental results show that the proposed system can not only continuously monitor the users,but also give early warning of physiological data that may indicate future diabetic ailments.

关键词： Deep learning incremental learning network architecture design chronic disease prediction

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DRSC: Dual-Reweighted Siamese Contrastive Learning network for Cross-Domain Rotating Machinery Fault Diagnosis With Multi-Source Domain Imbalanced Data

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IEEE Internet of Things Journal 2025年

作者： Bi, Yuanguo Fu, Rao Jiang, Cunyu Zhang, Xiaoling Li, Fengyun Zhao, Liang Han, Guangjie Northeastern University School of Computer Science and Engineering Shenyang110169 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110169 China Shenyang University of Technology Institute of Artificial Intelligence Shenyang110870 China Shenyang Aerospace University School of Computer Science Shenyang110135 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multi-source domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a Dual-Reweighted Siamese Contrastive learning network (DRSC) for cross-domain fault diagnosis with multi-source domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis. © 2014 IEEE.

关键词： Convolutional neural networks

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UDT:U-shaped deformable transformer for subarachnoid haemorrhage image segmentation

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CAAI Transactions on Intelligence technology 2024年第3期9卷 756-768页

作者： Wei Xie Lianghao Jin Shiqi Hua Hao Sun Bo Sun Zhigang Tu Jun Liu Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal UniversityWuhanChina School of Computer Science Central China Normal UniversityWuhanChina National Language Resources Monitoring and Research Center for Network Media Central China Normal UniversityWuhanChina The First Affiliated Hospital of Dalian Medical University DalianChina State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina Information Systems Technology and Design Pillar Singapore University of Technology and DesignSingaporeSingapore

Subarachnoid haemorrhage(SAH),mostly caused by the rupture of intracranial aneu-rysm,is a common disease with a high fatality *** lesions are generally diffusely distributed,showing a variety of scales with irregular *** complex characteristics of lesions make SAH segmentation a challenging *** cope with these difficulties,a u-shaped deformable transformer(UDT)is proposed for SAH ***,first,a multi-scale deformable attention(MSDA)module is exploited to model the diffuseness and scale-variant characteristics of SAH lesions,where the MSDA module can fuse features in different scales and adjust the attention field of each element dynamically to generate discriminative multi-scale ***,the cross deformable attention-based skip connection(CDASC)module is designed to model the irregular edge char-acteristic of SAH lesions,where the CDASC module can utilise the spatial details from encoder features to refine the spatial information of decoder ***,the MSDA and CDASC modules are embedded into the backbone Res-UNet to construct the proposed *** experiments are conducted on the self-built SAH-CT dataset and two public medical datasets(GlaS and MoNuSeg).Experimental results show that the presented UDT achieves the state-of-the-art performance.

关键词： image segmentation medical image processing

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Fault diagnosis for lithium-ion batteries in electric vehicles based on signal decomposition and two-dimensional feature clustering

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Green Energy and Intelligent Transportation 2022年第1期1卷 121-132页

作者： Shuowei Li Caiping Zhang Jingcai Du Xinwei Cong Linjing Zhang Yan Jiang Leyi Wang National Active Distribution Network Technology Research Center(NANTEC) Beijing Jiaotong UniversityBeijing 100044China Sunwoda Electronic Co. Ltd.Shenzhen518100China Department of Electrical&Computer Engineering Wayne State UniversityDetroitMI 48202USA

Battery fault diagnosis is essential for ensuring the reliability and safety of electric vehicles(EVs).The existing battery fault diagnosis methods are difficult to detect faults at an early stage based on the real-world vehicle data since lithium-ion battery systems are usually accompanied by inconsistencies,which are difficult to distinguish from faults.A fault diagnosis method based on signal decomposition and two-dimensional feature clustering is introduced in this *** geometry mode decomposition(SGMD)is introduced to obtain the components characterizing battery states,and distance-based similarity measures with the normalized extended average voltage and dynamic time warping distances are established to evaluate the state of *** 2-dimensional feature clustering based on DBSCAN is developed to reduce the number of feature thresholds and differentiate flaw cells from the battery pack with only one parameter under a wide range of *** proposed method can achieve fault diagnosis and voltage anomaly identification as early as 43 days ahead of the thermal *** the results of four electric vehicles and the comparison with other traditional methods validated the proposed method with strong robustness,high reliability,and long time scale warning,and the method is easy to implement online.

关键词： Electric vehicle Fault diagnosis Extended average voltage Dynamic time warping Feature clustering

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A Three-Stage Cutting Simulation System Based on Mass-Spring Model

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computer Modeling in engineering & Sciences 2021年第4期127卷 117-133页

作者： Xiaorui Zhang Jiali Duan Wei Sun Tong Xu Sunil Kumar Jha Jiangsu Engineering Center of Network Monitoring Engineering Research Center of Digital ForensicsMinistry of EducationSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China Faculty of Information Technology University of Information Technology and ManagementRzeszow35-225Poland

The cutting simulation of soft tissue is important in virtual *** includes three major challenges in computation:Soft tissue simulation,collision detection,and handling,as well as soft tissue *** order to address the earlier challenges,we propose a virtual cutting system based on the mass-spring *** this system,MSM is utilized to simulate the soft tissue *** stress is introduced to the model for simulating the shrinking effect of soft tissue in ***,a cylinder-based collision detection method is used to supervise the collision between surgical tools and soft ***,we simulate the cutting operation with a three-stage cutting method with swept volume,B´ezier curve,and an algorithm named shortest distance nodes matching *** order to verify the system performance,we carry out three validation experiments on the proposed system:Cutting accuracy experiment,collision detection validation,and practical cutting *** indicate that our system can well perform the shrinking effect of soft tissue in *** system has fast and accurate collision ***,the system can reconstruct smooth incisions vividly.

关键词： Mass-spring model collision algorithm virtual surgery soft tissue simulation

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