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Reinforcement learning based edge computing in B5G

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Digital Communications and Networks 2024年第1期10卷 1-6页

作者： Jiachen Yang Yiwen Sun Yutian Lei Zhuo Zhang Yang Li Yongjun Bao Zhihan Lv School of Electrical and Information Engineering Tianjin UniversiyTianjin300072China Technology and data center ***Bejing100176China School of data Science and Software Engineering Qingdao UniversityQingdao266101China

The development of communication technology will promote the application of Internet of Things,and Beyond 5G will become a new technology promoter.At the same time,Beyond 5G will become one of the important supports for the development of edge computing technology.This paper proposes a communication task allocation algorithm based on deep reinforcement learning for vehicle-to-pedestrian communication scenarios in edge computing.Through trial and error learning of agent,the optimal spectrum and power can be determined for transmission without global information,so as to balance the communication between vehicle-to-pedestrian and vehicle-to-infrastructure.The results show that the agent can effectively improve vehicle-to-infrastructure communication rate as well as meeting the delay constraints on the vehicle-to-pedestrian link.

关键词： Reinforcement learning Edge computing Beyond 5G Vehicle-to-pedestrian

Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

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Computers, Materials & Continua 2024年第5期79卷 2063-2083页

作者： Xiaoqin Ma Jun Wang Wenchang Yu Qinli Zhang School of Big data and Artificial Intelligence Chizhou UniversityChizhou247000China Anhui Education Big data Intelligent Perception and Application Engineering Research center Chizhou247000China

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that

关键词： Hybrid decision information systems fuzzy conditional information entropy attribute reduction fuzzy relationship rough set theory(RST)

Time-sensitive prediction of NO_(2) concentration in China using an ensemble machine learning model from multi-source data

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Journal of Environmental Sciences 2024年第3期137卷 30-40页

作者： Chenliang Tao Man Jia Guoqiang Wang Yuqiang Zhang Qingzhu Zhang Xianfeng Wang Qiao Wang Wenxing Wang Big data Research center for Ecology and Environment Environment Research InstituteShandong UniversityQingdao 266237China Shandong Provincial Eco-environment Monitoring center Jinan 250101China

Nitrogen dioxide(NO_(2))poses a critical potential risk to environmental quality and public health.A reliable machine learning(ML)forecasting framework will be useful to provide valuable information to support government decision-making.Based on the data from1609 air quality monitors across China from 2014-2020,this study designed an ensemble ML model by integrating multiple types of spatial-temporal variables and three sub-models for time-sensitive prediction over a wide range.The ensemble ML model incorporates a residual connection to the gated recurrent unit(GRU)network and adopts the advantage of Transformer,extreme gradient boosting(XGBoost)and GRU with residual connection network,resulting in a 4.1%±1.0%lower root mean square error over XGBoost for the test results.The ensemble model shows great prediction performance,with coefficient of determination of 0.91,0.86,and 0.77 for 1-hr,3-hr,and 24-hr averages for the test results,respectively.In particular,this model has achieved excellent performance with low spatial uncertainty in Central,East,and North China,the major site-dense zones.Through the interpretability analysis based on the Shapley value for different temporal resolutions,we found that the contribution of atmospheric chemical processes is more important for hourly predictions compared with the daily scale predictions,while the impact of meteorological conditions would be ever-prominent for the latter.Compared with existing models for different spatiotemporal scales,the present model can be implemented at any air quality monitoring station across China to facilitate achieving rapid and dependable forecast of NO_(2),which will help developing effective control policies.

关键词： Air quality prediction Deep learning Ensemble method Nitrogen dioxide Spatiotemporal covariates

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A Distributed Computing Algorithm for Electricity Carbon Emission Flow and Carbon Emission Intensity

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Protection and Control of Modern Power Systems 2024年第2期9卷 138-146页

作者： Xingping Wu Wei Yang Ning Zhang Chunlei Zhou Jinwei Song Chongqing Kang Big data center of China Network Chinese Society of Electrical Engineering State Network Big data center Department of Electrical Engineering Tsinghua UniversityBeijingChina 不详 Big data center State Grid Corporation of ChinaBeijing 100052China Department of Electrical Engi-neering Tsinghua UniversityBeijingChina

The calculation of the indirect carbon emis-sion is essential for power system policy making,carbon market development,and power grid planning.The em-bedded carbon emissions of the electricity system are commonly calculated by carbon emission flow theory.However,the calculation procedure is time-consuming,especially for a country with 500-1000 thousand nodes,making it challenging to obtain nationwide carbon emis-sions intensity precisely.Additionally,the calculation procedure requires to gather all the grid data with high classified levels from different power grid companies,which can prevent data sharing and cooperation among different companies.This paper proposes a distributed computing algorithm for indirect carbon emission that can reduce the time consumption and provide privacy protection.The core idea is to utilize the sparsity of the nodes’flow matrix of the nationwide grid to partition the computing procedure into parallel sub-procedures exe-cuted in multiple terminals.The flow and structure data of the regional grid are transformed irreversibly for pri-vacy protection,when transmitted between terminals.A 1-master-and-N-slave layout is adopted to verify the method.This algorithm is suitable for large grid compa-nies with headquarter and branches in provinces,such as the State Grid Corporation of China.

关键词： Carbon emission flow cooperative computing carbon emission intensity matrix block par-tition power flow tracing parallel computing privacy protection

Sensitivity impacts owing to the variations in the type of zero-range pairing forces on the fission properties using the density functional theory

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Nuclear Science and Techniques 2024年第3期35卷 198-207页

作者： Yang Su Ze-Yu Li Li-Le Liu Guo-Xiang Dong Xiao-Bao Wang Yong-Jing Chen China Nuclear data center China Institute of Atomic EnergyBeijing 102413China School of Science Huzhou UniversityHuzhou 313000China

Using the Skyrme density functional theory,potential energy surfaces of^(240)Pu with constraints on the axial quadrupole and octupole deformations(q_(20)and q_(30))were calculated.The volume-like and surface-like pairing forces,as well as a combination of these two forces,were used for the Hartree–Fock–Bogoliubov approximation.Variations in the least-energy fission path,fission barrier,pairing energy,total kinetic energy,scission line,and mass distribution of the fission fragments based on the different forms of the pairing forces were analyzed and discussed.The fission dynamics were studied based on the timedependent generator coordinate method plus the Gaussian overlap approximation.The results demonstrated a sensitivity of the mass and charge distributions of the fission fragments on the form of the pairing force.Based on the investigation of the neutron-induced fission of^(239)Pu,among the volume,mixed,and surface pairing forces,the mixed pairing force presented a good reproduction of the experimental data.

关键词： Nuclear fission Density functional theory Pairing force Potential energy surfaces Fission fragment distribution

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IoTDQ: An Industrial IoT data Analysis Library for Apache IoTDB

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Big data Mining and Analytics 2024年第1期7卷 29-41页

作者： Pengyu Chen Wendi He Wenxuan Ma Xiangdong Huang Chen Wang School of Software Tsinghua UniversityBeijing 100084China National Engineering Research center for Big data Software(NERCBDS) Tsinghua UniversityBeijing 100084China

There is a growing demand for time series data analysis in industry areas.Apache loTDB is a time series database designed for the Internet of Things(loT)with enhanced storage and I/O performance.With User-Defined Functions(UDF)provided,computation for time series can be executed on Apache loTDB directly.To satisfy most of the common requirements in industrial time series analysis,we create a UDF library,loTDQ,on Apache loTDB.This library integrates stream computation functions on data quality analysis,data profiling,anomaly detection,data repairing,etc.loTDQ enables users to conduct a wide range of analyses,such as monitoring,error diagnosis,equipment reliability analysis.It provides a framework for users to examine loT time series with data quality problems.Experiments show that loTDQ keeps the same level of performance compared to mainstream alternatives,and shortens I/O consumption for Apache loTDB users.

关键词： industrial big data data quality data mining and analytics

Survey on Fake Information Generation,Dissemination and Detection

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Chinese Journal of Electronics 2024年第3期33卷 573-583页

作者： Wanqiu CUI Dawei WANG Na HAN School of National Security People's Public Security University of China Big data center of The Ministry of Public Security

The current booming development of the Internet has put the public in an era of information overload,in which false information is mixed and spread unscrupulously.This phenomenon has seriously disturbed the social network order.Thus,a substantial amount of research is beginning to be devoted to the effective management of fake information.We analyze the abnormal characteristics of fake information from its mechanism of generation and dissemination.In view of different exceptional features,we systematically sort out and evaluate the existing studies on false content detection.The commonly used public datasets,metrics,and performance are categorized and compared,hoping to provide a basis and guidance for related research.The study found that the current active social platforms show different novelty.The future direction should point to mining platform features of multi-domain sources,multi-data forms,and multi-language heterogeneity to provide more valuable clues for fake information.

关键词： Fake information Abnormal characteristics Detection methods Public datasets

A study of U-isotope ground state properties with covariant energy density functional

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Chinese Physics C 2024年第6期48卷 130-135页

作者：王俊辰田源续瑞瑞崔莹陶曦孙小东张智张玥韩银录葛智刚舒能川 China Nuclear data center China Institute of Atomic EnergyBeijing 102413China China Institute of Atomic Energy Beijing 102413China

In this study,we systematically analyzed the ground state of uranium isotopes from 225 to 240.In our calculations,we used the covariant energy density functional of density-dependent meson exchange interaction(DDME2)with separable pairing interaction(TMR).Using the multiple deformation constraint,we calculated the potential energy surface(PES)of the uranium isotopes for both even-even and even-odd nuclei with quadrupole and octupole deformation.Based on our calculation and upon comparing the experimental data and Hartree-Fock-Bogoliubov calculations with Gogny D1S calculation data,the ground state of uranium isotopes with reflection-asymmetric deformation was found to be preferred.

关键词： relativistic mean field density functional theory octupole deformation

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An Improved Binary Quantum-based Avian Navigation Optimizer Algorithm to Select Effective Feature Subset from Medical data:A COVID-19 Case Study

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Journal of Bionic Engineering 2024年第1期21卷 426-446页

作者： Ali Fatahi Mohammad H.Nadimi-Shahraki Hoda Zamani Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad ***Iran Big data Research center Najafabad BranchIslamic Azad UniversityNajafabad ***Iran

Feature Subset Selection(FSS)is an NP-hard problem to remove redundant and irrelevant features particularly from medical data,and it can be effectively addressed by metaheuristic algorithms.However,existing binary versions of metaheuristic algorithms have issues with convergence and lack an effective binarization method,resulting in suboptimal solutions that hinder diagnosis and prediction accuracy.This paper aims to propose an Improved Binary Quantum-based Avian Navigation Optimizer Algorithm(IBQANA)for FSS in medical data preprocessing to address the suboptimal solutions arising from binary versions of metaheuristic algorithms.The proposed IBQANA’s contributions include the Hybrid Binary Operator(HBO)and the Distance-based Binary Search Strategy(DBSS).HBO is designed to convert continuous values into binary solutions,even for values outside the[0,1]range,ensuring accurate binary mapping.On the other hand,DBSS is a two-phase search strategy that enhances the performance of inferior search agents and accelerates convergence.By combining exploration and exploitation phases based on an adaptive probability function,DBSS effectively avoids local optima.The effectiveness of applying HBO is compared with five transfer function families and thresholding on 12 medical datasets,with feature numbers ranging from 8 to 10,509.IBQANA's effectiveness is evaluated regarding the accuracy,fitness,and selected features and compared with seven binary metaheuristic algorithms.Furthermore,IBQANA is utilized to detect COVID-19.The results reveal that the proposed IBQANA outperforms all comparative algorithms on COVID-19 and 11 other medical datasets.The proposed method presents a promising solution to the FSS problem in medical data preprocessing.

关键词： Feature subset selection Optimization Binary metaheuristic algorithms Bioinspired Machine learning Medical datasets