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Deep learning framework for automatic detection and classification of sleep apnea severity from polysomnography signals

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Neural Computing and Applications 2024年第28期36卷 17483-17493页

作者： Raja Brundha, A. Lakshmi Sangeetha, A. Balajiganesh, A. Department of Artificial Intelligence and Data Science Sri Sai Ram Engineering College Chennai India Department of Computer Science Engineering Velammal Engineering College Chennai India

Sleep apnea (SA) is a sleep-related breathing disorder characterized by breathing pauses during sleep. A person’s sleep schedule is significantly influenced by that person’s hectic lifestyle, which may include unhealthy eating habits and their line of work. Polysomnographic (PSG) sleep studies examine sleep-related disorders by recording various biosignals from the human body. However, SA classification methods could be more robust in terms of performance because they rely on feature-engineering strategies or employ a particular signal from PSG recording for diagnosis. This study aims to classify the severity of SA according to the apnea–hypopnea index (AHI) into normal, mild, and moderate-to-severe groups using oxygen saturation (SpO2), electroencephalogram (EEG), and electrocardiogram (ECG) signals. The proposed deep neural network (DNN)-bidirectional long short-term memory (DNN-BiLSTM) framework addresses the issue of low detection accuracy in analysis. The DNN-BiLSTM approach employs features extracted from a multiscale dilation attention 1D convolutional neural network (MSDA-1DCNN) as input for detection and classification purposes. The MSDA-1DCNN network extracts deep features from processed SpO2, EEG, and ECG signals. The developed firefly combined electric fish optimization (FCEFO) algorithm improves performance by optimizing the hidden neuron count of the DNN and the learning rate of the BiLSTM framework. The performance measures proved the effectiveness of the model over conventional machine and deep learning approaches. With the integration of deep features, the proposed DNN-BiLSTM model provides enhanced performance in terms of accuracy and precision. Thus, the proposed approach is progressive and can be used for medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electrocardiograms

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Investigation into the Methodology and Implementation of Life Cycle engineering under China’s Carbon Reduction Target in the Process Industry

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engineering 2024年第9期40卷 87-99页

作者： Mingyang Li Feng Gao Zuoren Nie Boxue Sun Yu Liu Xianzheng Gong National Engineering Laboratory for Industrial Big-data Application Technology College of Material Science and EngineeringBeijing University of TechnologyBeijing 100124China

The industrial sector is the primary source of carbon emissions in *** pursuit of meeting its carbon reduction targets,China aims to promote resource consumption sustainability,reduce energy consumption,and achieve carbon neutrality within its processing *** effective strategy to promote energy savings and carbon reduction throughout the life cycle of materials is by applying life cycle engineering *** strategy aims to attain an optimal solution for material performance,resource consumption,and environmental *** this study,five types of technologies were considered:raw material replacement,process reengineering,fuel replacement,energy recycling and reutilization,and material recycling and *** meaning,methodology,and development status of life cycle engineering technology abroad and domestically are discussed in detail.A multidimensional analysis of ecological design was conducted from the perspectives of resource and energy consumption,carbon emissions,product performance,and recycling of secondary resources in a manufacturing *** coupled with an integrated method to analyze carbon emissions in the entire life cycle of a material process industry was applied to the nonferrous industry,as an *** results provide effective ideas and solutions for achieving low or zero carbon emission production in the Chinese industry as recycled aluminum and primary aluminum based on advanced technologies had reduced resource consumption and emissions as compared to primary aluminum production.

关键词： Carbon neutrality Life cycle engineering Process Industry Carbon reduction technology Eco-design

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Physical information-enhanced graph neural network for predicting phase separation

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Chinese Physics B 2024年第7期33卷 278-283页

作者：张亚强王煦文王雅楠郑文 Institute of Public-Safety and Big Data College of Computer Science and Technology(College of Data Science)Taiyuan University of TechnologyJinzhong 030600China Shanxi Engineering Research Centre for Intelligent Data Assisted Treatment Changzhi Medical CollegeChangzhi 046000China Innovation Academy for Microsatellites of Chinese Academy of Sciences Shanghai 200050China

Although phase separation is a ubiquitous phenomenon, the interactions between multiple components make it difficult to accurately model and predict. In recent years, machine learning has been widely used in physics simulations. Here,we present a physical information-enhanced graph neural network(PIENet) to simulate and predict the evolution of phase separation. The accuracy of our model in predicting particle positions is improved by 40.3% and 51.77% compared with CNN and SVM respectively. Moreover, we design an order parameter based on local density to measure the evolution of phase separation and analyze the systematic changes with different repulsion coefficients and different Schmidt *** results demonstrate that our model can achieve long-term accurate predictions of order parameters without requiring complex handcrafted features. These results prove that graph neural networks can become new tools and methods for predicting the structure and properties of complex physical systems.

关键词： graph neural network phase separation machine learning dissipative particle dynamics

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A survey on cross-user federated recommendation

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science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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Efficiency-optimized 6G:A virtual network resource orchestration strategy by enhanced particle swarm optimization

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Digital Communications and Networks 2024年第5期10卷 1221-1233页

作者： Sai Zou Junrui Wu Haisheng Yu Wenyong Wang Lisheng Huang Wei Ni Yan Liu College of Big Data and Information Engineering Guizhou UniversityGuiyangChina Prospective Technology Lab of the China Future Internet Engineering Center BeijingChina College of Computer Science and Engineering University of Electronic Science and TechnologyChengduChina International Institute for Next Generation Internet Macao University of Science and TechnologyMacaoChina Data61 Business Unit CSIROAustralia

The future Sixth-Generation (6G) wireless systems are expected to encounter emerging services with diverserequirements. In this paper, 6G network resource orchestration is optimized to support customized networkslicing of services, and place network functions generated by heterogeneous devices into available *** is a combinatorial optimization problem that is solved by developing a Particle Swarm Optimization (PSO)based scheduling strategy with enhanced inertia weight, particle variation, and nonlinear learning factor, therebybalancing the local and global solutions and improving the convergence speed to globally near-optimal *** show that the method improves the convergence speed and the utilization of network resourcescompared with other variants of PSO.

关键词： Virtualization Network function orchestration Network resource virtualized orchestration (NRVO) Particle swarm optimization(PSO)

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Towards Optimization and Assessment of the Chinese New-generation VLBI Network for Earth Orientation Parameters Monitoring

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Research in Astronomy and Astrophysics 2025年第2期25卷 256-268页

作者： Hao Xiang Zhong-Kai Zhang Lan Du Ze-Jun Liu Xu-Qiao Wang College of Geospatial Information Information Engineering University Henan Industrial Technology Academy of Spatio-Temporal Big Data College of Geography and Environmental Science Henan University

The Earth Orientation Parameters(EOP) provide a time-varying transition relationship between the International Terrestrial Reference Frame and the International Celestial Reference Frame. To support deep space exploration and the Beidou Navigation Satellite System, the Chinese New-generation Very Long Baseline Interferometry Network(CNVN) is under construction for independent monitoring of the EOP. This paper evaluates the performance of existing 4-antenna CNVN through a batch generated observation schedules followed by extensive Monte Carlo simulations. The optimal positions of the fifth and sixth antennas of CNVN are found from 24hypothetical antenna positions uniformly distributed in China. In this process, the weighted parameters are optimized, which not only reduce the possibility of large error of EOP estimation accuracy due to unreasonable combination, but also greatly reduce the calculation cost.

关键词： methods: analytical techniques: interferometric reference systems

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Classification and structural characteristics of amorphous materials based on interpretable deep learning

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Chinese Physics B 2023年第9期32卷 356-363页

作者：崔佳梅李韵洁赵偲郑文 Institute of Public Safety and Big Data College of Data ScienceTaiyuan University of TechnologyJinzhong 030600China Shanxi Engineering Research Center for Intelligent Data Assisted Treatment Changzhi Medical CollegeChangzhi 046000China

Defining the structure characteristics of amorphous materials is one of the fundamental problems that need to be solved urgently in complex materials because of their complex structure and long-range *** this study,we develop an interpretable deep learning model capable of accurately classifying amorphous configurations and characterizing their structural *** results demonstrate that the multi-dimensional hybrid convolutional neural network can classify the two-dimensional(2D)liquids and amorphous solids of molecular dynamics *** classification process does not make a priori assumptions on the amorphous particle environment,and the accuracy is 92.75%,which is better than other convolutional neural ***,our model utilizes the gradient-weighted activation-like mapping method,which generates activation-like heat maps that can precisely identify important structures in the amorphous configuration *** obtain an order parameter from the heatmap and conduct finite scale analysis of this *** findings demonstrate that the order parameter effectively captures the amorphous phase transition process across various *** results hold significant scientific implications for the study of amorphous structural characteristics via deep learning.

关键词： amorphous interpretable deep learning image classification finite scale analysis

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Neural network-based soil parameters predictive coordination algorithm for energy efficient wireless sensor network

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Journal of Ambient Intelligence and Humanized Computing 2024年第11期15卷 3733-3743页

作者： Sharma, Dinesh Tomar, Geetam Singh Department of Data Science and Engineering Manipal University Jaipur Jaipur India Rajkiya Engineering College Uttar Pradesh Sonbhadra India

The utilization of Wireless Sensor Networks (WSN) in the agricultural field represents a significant stride in the application of Information Technology. Recent advancements in technology have made it possible for sensor networks not only to provide real-time information about soil nutrient levels but also to assist in the automation of various agricultural processes. However, it’s crucial to acknowledge a substantial limitation associated with WSN, namely, energy consumption. Through the analysis of experimental data gathered from diverse soil types and employing sophisticated data analytics, it has been observed that the Nutrient Index exhibits a relatively stable pattern over time. Consequently, predictive neural network techniques can be employed to extract detailed insights from the primary inputs received from WSN. This approach eliminates the need for continuous operation of the WSN throughout the day, contributing to enhanced energy efficiency. To achieve this energy-efficient operation, the NR-MDEC protocol is implemented in conjunction with a coordination algorithm, resulting in a substantial improvement in overall efficiency. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Fertilizers

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Performance Evaluation of a Multi-input Interleaved Boost Converter with a Tuned Proportional-integral Controller

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Chinese Journal of Electrical engineering 2024年第4期10卷 119-128页

作者： Sucharita Pal Dola Sinha Mou Das Mahapatra Saibal Majumder Sovan Bhattacharya Chan Byopadhyay Department of Electrical Engineering Asansol Engineering CollegeAsansol 713305India Department of Electrical Engineering Dr.B.C.Roy Engineering CollegeDurgapur 713206India Department of Computer Science and Engineering(Data Science) Dr.B.C.Roy Engineering CollegeDurgapur 713206India Department of Computer Science and Engineering University of BremenBremen 28359Germany

The need for renewable energy access has led to the use of variable input converter approaches because renewable energy sources often generate electricity in an unpredictable manner. A high-performance multi-input boost converter is developed to provide the necessary output voltage and power while accommodating variations in input sources. This converter is specifically designed for the efficient usage of renewable energy. The proposed architecture integrates three separate unidirectional input power sources: photovoltaics, fuel cells, and storage system batteries. The architecture has five switches, and the implementation of each switch in the converter is achieved by applying the calculated duty ratios in various operating states. The closed-loop response of the converter with a proportional-integral (PI) controller-based switching system is examined by analyzing the Matlab-Simulink model utilizing a proportional-integral derivative (PID) tuner. The controller can deliver the desired output voltage of 400 V and an average power of 2 kW while exhibiting low switching transient effects. Therefore, the proposed multi-input interleaved boost converter demonstrates robust results for real-time applications by effectively harnessing renewable power sources.

关键词： Multi-input converter interleaved boost converter pulse-width modulation proportional-integral controller

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Rucklidge-based memristive chaotic system:Dynamic analysis and image encryption

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Chinese Physics B 2023年第10期32卷 298-309页

作者：蹇璨岭田泽安梁波胡晨阳王桥陈靖翕 College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China College of Mathematics and Big Data Guizhou Education UniversityGuiyang 550018China College of Mechanical Engineering Guizhou UniversityGuiyang 550025China

A new four-dimensional(4D)memristive chaotic system is obtained by introducing a memristor into the Rucklidge chaotic system,and a detailed dynamic analysis of the system is *** sensitivity of the system to parameters allows it obtains 16 different attractors by changing only one *** various transient behaviors and excellent spectral entropy and C0 complexity values of the system can also reflect the high complexity of the system.A circuit is designed and verified the feasibility of the system from the physical ***,the system is applied to image encryption,and the security of the encryption system is analyzed from multiple aspects,providing a reference for the application of such memristive chaotic systems.

关键词： chaotic system memristor intermittent chaos multi-transient behavior image encryption

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